Royston Morgan

On March 24 a FINDALL search in Google for keywords density optimization returned 240,000 documents. I found many of these documents belonging to search engine marketing and optimization (SEM, SEO) specialists. Some of them promote keyword density (KD) analysis tools while others talk about things like “right density weighting”, “excellent keyword density”, KD as a “concentration” or “strength” ratio and the like. Others even take KD for the weight of term i in document j, while others propose localized KD ranges for titles, descriptions, paragraphs, tables, links, urls, etc. One can even find some specialists going after the latest KD “trick” and claiming that optimizing KD values up to a certain range for a given search engine affects the way a search engine scores relevancy and ranks documents.

Given the fact that there are so many KD theories flying around, my good friend Mike Grehan approached me after the Jupitermedia’s 2005 Search Engine Strategies Conference held in New York and invited me to do something about it. I felt the “something” should be a balanced article mixed with a bit of IR, semantics and math elements but with no conclusion so readers could draw their own. So, here we go.

Background.

In the search engine marketing literature, keyword density is defined as

Equation 1

where tfi, j is the number of times term i appears in document j and l is the total number of terms in the document. Equation 1 is a legacy idea found intermingled in the old literature on readability theory, where word frequency ratios are calculated for passages and text windows – phrases, sentences, paragraphs or entire documents – and combined with other readability tests.

The notion of keyword density values predates all commercial search engines and the Internet and can hardly be considered an IR concept. What is worse, KD plays no role on how commercial search engines process text, index documents or assign weights to terms. Why then many optimizers still believe in KD values? The answer is simple: misinformation.

If two documents, D1 and D2, consist of 1000 terms (l = 1000) and repeat a term 20 times (tf = 20), then for both documents KD = 20/1000 = 0.020 (or 2%) for that term. Identical values are obtained if tf = 10 and l = 500.

Evidently, this overall ratio tells us nothing about:

1. the relative distance between keywords in documents (proximity)

2. where in a document the terms occur (distribution)

3. the co-citation frequency between terms (co-occurrence)

4. the main theme, topic, and sub-topics (on-topic issues) of the documents

Thus, KD is divorced from content quality, semantics and relevancy. Under these circumstances one can hardly talk about optimizing term weights for ranking purposes. Add to this copy style issues and you get a good idea of why this article’s title is The Keyword Density of Non-Sense.

The following five search engine implementations illustrate the point:

1. Linearization

2. Tokenization

3. Filtration

4. Stemming

5. Weighting

Linearization.

Linearization is the process of ignoring markup tags from a web document so its content is reinterpreted as a string of characters to be scored. This process is carried out tag-by-tag and as tags are declared and found in the source code. As illustrated in Figure 1, linearization affects the way search engines “see”, “read” and “judge” Web content –sort of speak. Here the content of a website is rendered using two nested html tables, each consisting of one large cell at the top and the common 3-column cell format. We assume that no other text and html tags are present in the source code. The numbers at the top-right corner of the cells indicate in which order a search engine finds and interprets the content of the cells.

The box at the bottom of Figure 1 illustrates how a search engine probably “sees”, “reads” and “interprets” the content of this document after linearization. Note the lack of coherence and theming. Two term sequences illustrate the point: “Find Information About Food on sale!” and “Clients Visit our Partners”. This state of the content is probably hidden from the untrained eyes of average users. Clearly, linearization has a detrimental effect on keyword positioning, proximity, distribution and on the effective content to be “judged” and scored. The effect worsens as more nested tables and html tags are used, to the point that after linearization content perceived as meritorious by a human can be interpreted as plain garbage by a search engine. Thus, computing localized KD values is a futile exercise.

Burning the Trees and Keyword Weight Fights.

In the best-case scenario, linearization shows whether words, phrases and passages end competing for relevancy in a distorted lexicographical tree. I call this phenomenon “burning the trees”. It is one of the most overlooked web design and optimization problems.

Constructing a lexicographical tree out of linearized content reveals the actual state and relationship between nouns, adjectives, verbs, and phrases as they are actually embedded in documents. It shows the effective data structure that is been used. In many cases, linearization identifies local document concepts (noun groups) and hidden grammatical patterns. Mandelbrot has used the patterned nature of languages observed in lexicographical trees to propose a measure he calls the “temperature of discourse”. He writes: “The `hotter’ the discourse, the higher the probability of use of rare words.” (1). However, from the semantics standpoint, word rarity is a context dependent state. Thus, in my view “burning the trees” is a natural consequence of misplacing terms.

In Fractals and Sentence Production, Chapter 9 of From Complexity to Creativity (2, 3), Ben Goertzel uses an L-System model to explain that the beginning of early childhood grammar is the two-word sentence in which the iterative pattern involving nouns (N) and verbs( V) is driven by a rule in which V is replaced by V N (V >> V N). This can be illustrated with the following two iteration stages:

0 N V (as in Stevie byebye)

1 N V N (as in Stevie byebye car)

Goertzel explains, “-The reason N V is a more natural combination is because it occurs at an earlier step in the derivation process.” (3). It is now comprehensible why many Web documents do not deliver any appealing message to search engines. After linearization, it can be realized that these may be “speaking” like babies. [By the way, L-System algorithms, named after A. Lindermayer, have been used for many years in the study of tree-like patterns (4)].

“Burning the trees” explains why repeating terms in a document, moving around on-page factors or invoking link strategies, not necessarily improves relevancy. In many instances one can get the opposite result. I recommend SEOs to start incorporating lexicographical/word pattern techniques, linearization strategies and local context analysis (LCA) into their optimization mix. (5)

In Figure 1, “burning the trees” was the result of improper positioning of text. However in many cases the effect is a byproduct of sloppy Web design, poor usability or of improper use of the HTML DOM structure (another kind of tree). This underscores an important W3C recommendation: that html tables should be use for presenting tabular data, not for designing Web documents. In most cases, professional web designers can do better by replacing tables with cascading style sheets (CSS).

“Burning the trees” often leads to another phenomenon I call “keyword weight fights”. It is a recurrent problem encountered during topic identification (topic spotting), text segmentation (based on topic changes) and on-topic analysis (6). Considering that co-occurrence patterns of words and word classes provide important information about how a language is used, misplaced keywords and text without clear topic transitions difficult the work of text summarization editors (humans or machine-based) that need to generate representative headings and outlines from documents.

Thus, the “fight” unnecessarily difficults topic disambiguation and the work of human abstractors that during document classification need to answer questions like “What is this document or passage about?”, “What is the theme or category of this document, section or paragraph?”, “How does this block of links relate to the content?”, etc.

While linearization renders localized KD values useless, document indexing makes a myth out of this metric. Let see why.

Tokenization, Filtration and Stemming

Document indexing is the process of transforming document text into a representation of text and consists of three steps: tokenization, filtration and stemming.

During tokenization terms are lowercased and punctuation removed. Rules must be in place so digits, hyphens and other symbols can be parsed properly. Tokenization is followed by filtration. During filtration commonly used terms and terms that do not add any semantic meaning (stopwords) are removed. In most IR systems survival terms are further reduced to common stems or roots. This is known as stemming. Thus, the initial content of length l is reduced to a list of terms (stems and words) of length l’ (i.e., l’ < l). These processes are described in Figure 2. Evidently, if linearization shows that you have already “burned the trees”, a search engine will be indexing just that.

Similar lists can be extracted from individual documents and merged to conform an index of terms. This index can be used for different purposes; for instance, to compute term weights and to represent documents and queries as term vectors in a term space.

Weighting.

The weight of a term in a document consists of three different types of term weighting: local, global, and normalization. The term weight is given by

Equation 2

where Li, j is the local weight for term i in document j, Gi is the global weight for term i and Nj is the normalization factor for document j. Local weights are functions of how many times each term occurs in a document, global weights are functions of how many times documents containing each term appears in the collection, and the normalization factor corrects for discrepancies in the lengths of the documents.

In the classic Term Vector Space model

Equation 3, 4 and 5

which reduces to the well-known tf*IDF weighting scheme

Equation 6

where log(D/di) is the Inverse Document Frequency (IDF), D is the number of documents in the collection (the database size) and di is the number of documents containing term i.

Equation 6 is just one of many term weighting schemes found in the term vector literature. Depending on how L, G and N are defined, different weighting schemes can be proposed for documents and queries.

KD values as estimators of term weights?

The only way that KD values could be taken for term weights

Equation 7

is if global weights are ignored and the normalization factor Nj is redefined in terms of document lengths

Equation 8

However, Gi = IDF = 1 constraints the collection size D to be equal to ten times the number of documents containing the term (D = 10*d) and Nj = 1/lj implies no stopword filtration. These conditions are not observed in commercial search systems.

Using a probabilistic term vector scheme in which IDF is defined as

Equation 9

does not help either since the condition Gi = IDF = 1 implies that D = 11*d. Additional unrrealistic constraints can be derived for other weighting schemes when Gi = 1.

To sum up, the assumption that KD values could be taken for estimates of term weights or that these values could be used for optimization purposes amounts to the Keyword Density of Non-Sense.

SWOT (Strengths Weaknesses Opportunities and Threats) Analysis is a simple but surprisingly effective technique to assess an organisations positioning and begin the process of turning general ideas for market growth into actionable activities. This brief guide shows how to extend the simple SWOT concept into a tool for defining the actions needed to deal with external threats and internal weaknesses in the organisations capabilities.

The process is best done within a workshop concept. So organise a team meeting of around 7 to 10 interested parties who are experts or knowledgeable in the domain to be considered.

The process:

Step 1 – First agree the area to be considered and the core assumptions. For example ‘we will consider the Softhouse organisation and the opportunities to grow the market in the States.

Step 2 – Use a brainstorming technique such as nominal group and ask the team first to think about the area we have chosen and what the issues in delivering this approach are. They write down (on their own) what could be the barriers or carriers to entering the new market in the States onto post-it notes or just make a list on paper before them.

Step 3 – They place their post it notes (or the facilitator) in turn onto the grid as shown in the diagram below – barriers to threats and carriers to opportunities.

Step 4 – Brainstorm as in step 2 and consider the organisation (Softhouse) and what are its unique strengths or capabilities and its weaknesses. The team on their own write down onto post-it notes their ideas as before.

Step 5 – They place their post-it notes (or the facilitator) in turn onto the grid as shown in the diagram – strengths to strengths and weaknesses to weaknesses.

Step 6 – The team then consider the crossing points of the SWOT for example between Threats and Strengths below (top left box) and as shown in the diagram think of specific actions to use strengths to counter any threats. These are written onto post it notes as before and placed in turn into the grid.

Step 7 – The facilitator tidies up the board removing duplicates or clarifying actions that have been written down. The board actions are then agreed prioritized then transferred to a standard action plan template.

Example Swot Action Analysis

Here is an example taken from an early draft of a business plan to illustrate the completed board. From here the actions can be taken across to a standard action plan template and owners and timescales applied. Thus from an initial consideration of the external and internal environment we can quite quickly move to a position where we can see possible practical actions we can take to move the agenda forward.

Giving an conference address

I have sat through and given a few presentations in my time so based on my experience of sitting through a conference or two I have put together a few tips:

Preparing For The Event

Read the proposed conference flyer and match your points to the theme. I sat through an interesting presentation the other-day that left me and the people on my table mystified as to how it fitted in the theme of the conference (it was good though).

The flyers can help on the direction of the content – it is always a good idea to discuss the content further with the Conference Producer before you prepare ‘it’.

Cicero over two thousand years ago said a good speaker learns fast and is knowledgeable and is an expert about the subject – know your subject in depth and provide evidence during your speech that you know what you are talking about.

Content

If you are speaking at a conference attracting senior-level decision-makers from across your sector ask yourself:

What do they want to hear?

What do you want to say?

Where does the crossover lie?

Watch out! – Presentations from speakers who dwell too long on their basic company information are always seen as crude sales pitches – and people switch off (No more than who you are and what you do please).

Be aware of the format of your session

If you are doing a presentation and are using PowerPoint:

Use a minimum font size of 18 – better 24+

Allow around three minutes per slide (remember no death by PowerPoint!).

The Rule of Five – ideally PowerPoint presentations should contain no more than 5 words per sentence and 5 lines per slide (actuall no words is better just a few pictures).

Visuals are often a great way of illustrating your presentation but ‘Keep It Simple’ – too many charts overwhelm a presentation and cannot be read at the back of the conference room.

Likewise, avoid over-use of PowerPoint special effects – or flash effects like zooming they distract from the presentation

If you are taking part in a panel discussion prepare:

The Chair should contact you approximately 2 weeks in advance of the panel to set the agenda – schedule time to talk to her!

You are likely to be asked to spend five minutes setting out your thoughts on the proposed topic.

Prepare and memorise this five minute piece and think carefully about what you are going to say (Cicero also recommended memorising your speech).

Practice makes perfect

Rehearse your speech several times preferably in front of an audience who will not fall asleep and who are honest.

And on the day…

…start strong

It is often helpful to memorise the first minute or two of your speech to ease you into it – once you’ve started you’ll find it easier to keep going. Never apologise or spend too much time on inane pleasantries – get down to business. The first minute or two is about establishing the rapport with the audience and setting the degree to which they give you authority to speak.

Think about your body language

Style and tone of voice account for 90 per cent of communication so adopt a relaxed, confident pose.

Maintain eye contact with the audience – select one or two people from the audience to maintain contact but do not stare!

If there are label mics available use them – no Al Jolson impressions and shout at them!

Timings

Watch your timing, never overun and finish a few minutes to ask for any questions

Closing note: On the question of number of words on a slide. Keep this to a minimum and if possible none at all other than an intro slide ‘who you are’. I was at a resilience conference a few months back and we had an address by a very senior woman from the States who used no slides at all (or notes) and she held the audience riveted by her authority on the topic. There was a hand-out at the end for notes but for the duration of the address there were no distractions and we were able to follow the logic closely.